The overall process involves making conjectures (hypotheses), deriving predictions from them as logical consequences, and then carrying out experiments based on those predictions to determine whether the original conjecture was correct.[6] There are difficulties in a formulaic statement of method, however. Though the scientific method is often presented as a fixed sequence of steps, they are better considered as general principles.[30] Not all steps take place in every scientific inquiry (or to the same degree), and are not always in the same order. As noted by William Whewell (1794–1866), “invention, sagacity, [and] genius”[11] are required at every step.
Formulation of a question

The question can refer to the explanation of a specific observation, as in “Why is the sky blue?”, but can also be open-ended, as in “How can I design a drug to cure this particular disease?” This stage frequently involves finding and evaluating evidence from previous experiments, personal scientific observations or assertions, and/or the work of other scientists. If the answer is already known, a different question that builds on the previous evidence can be posed. When applying the scientific method to scientific research, determining a good question can be very difficult and affects the final outcome of the investigation.[31]
Hypothesis

A hypothesis is a conjecture, based on knowledge obtained while formulating the question, that may explain the observed behavior of a part of our universe. The hypothesis might be very specific, e.g., Einstein’s equivalence principle or Francis Crick’s “DNA makes RNA makes protein”,[32] or it might be broad, e.g., unknown species of life dwell in the unexplored depths of the oceans. A statistical hypothesis is a conjecture about some population. For example, the population might be people with a particular disease. The conjecture might be that a new drug will cure the disease in some of those people. Terms commonly associated with statistical hypotheses are null hypothesis and alternative hypothesis. A null hypothesis is the conjecture that the statistical hypothesis is false, e.g., that the new drug does nothing and that any cures are due to chance effects. Researchers normally want to show that the null hypothesis is false. The alternative hypothesis is the desired outcome, e.g., that the drug does better than chance. A final point: a scientific hypothesis must be falsifiable, meaning that one can identify a possible outcome of an experiment that conflicts with predictions deduced from the hypothesis; otherwise, it cannot be meaningfully tested.
Prediction

This step involves determining the logical consequences of the hypothesis. One or more predictions are then selected for further testing. The more unlikely that a prediction would be correct simply by coincidence, then the more convincing it would be if the prediction were fulfilled; evidence is also stronger if the answer to the prediction is not already known, due to the effects of hindsight bias (see also postdiction). Ideally, the prediction must also distinguish the hypothesis from likely alternatives; if two hypotheses make the same prediction, observing the prediction to be correct is not evidence for either one over the other. (These statements about the relative strength of evidence can be mathematically derived using Bayes’ Theorem).[33]
Testing

This is an investigation of whether the real world behaves as predicted by the hypothesis. Scientists (and other people) test hypotheses by conducting experiments. The purpose of an experiment is to determine whether observations of the real world agree with or conflict with the predictions derived from a hypothesis. If they agree, confidence in the hypothesis increases; otherwise, it decreases. Agreement does not assure that the hypothesis is true; future experiments may reveal problems. Karl Popper advised scientists to try to falsify hypotheses, i.e., to search for and test those experiments that seem most doubtful. Large numbers of successful confirmations are not convincing if they arise from experiments that avoid risk.[9] Experiments should be designed to minimize possible errors, especially through the use of appropriate scientific controls. For example, tests of medical treatments are commonly run as double-blind tests. Test personnel, who might unwittingly reveal to test subjects which samples are the desired test drugs and which are placebos, are kept ignorant of which are which. Such hints can bias the responses of the test subjects. Furthermore, failure of an experiment does not necessarily mean the hypothesis is false. Experiments always depend on several hypotheses, e.g., that the test equipment is working properly, and a failure may be a failure of one of the auxiliary hypotheses. (See the Duhem–Quine thesis.) Experiments can be conducted in a college lab, on a kitchen table, at CERN’s Large Hadron Collider, at the bottom of an ocean, on Mars (using one of the working rovers), and so on. Astronomers do experiments, searching for planets around distant stars. Finally, most individual experiments address highly specific topics for reasons of practicality. As a result, evidence about broader topics is usually accumulated gradually.
Analysis

This involves determining what the results of the experiment show and deciding on the next actions to take. The predictions of the hypothesis are compared to those of the null hypothesis, to determine which is better able to explain the data. In cases where an experiment is repeated many times, a statistical analysis such as a chi-squared test may be required. If the evidence has falsified the hypothesis, a new hypothesis is required; if the experiment supports the hypothesis but the evidence is not strong enough for high confidence, other predictions from the hypothesis must be tested. Once a hypothesis is strongly supported by evidence, a new question can be asked to provide further insight on the same topic. Evidence from other scientists and experience are frequently incorporated at any stage in the process. Depending on the complexity of the experiment, many iterations may be required to gather sufficient evidence to answer a question with confidence, or to build up many answers to highly specific questions in order to answer a single broader question.

In short,

Make an Observation

Form a Question

Form a Hypothesis

Conduct an Experiment

Analyse the Data and Draw a Conclusion.

There are certain requirements that any theory must satisfy before it can be deemed a scientific theory, let along an accurate one. The arguably most important criteria is, that your theory must make a verifiable prediction. If your theory does not make a testable verifiable prediction, then it is not a scientific theory. Equally important, is that if independent testing fails to reproduce your results, your theory is said to have been falsified, or proven false.

This is the foundation of what is known as the “Peer Review” process. As it was originally designed, the Peer Review process is not designed to confirm that your theory is correct, but that your peer’s involved in similar or related research are unable to falsify or prove that your results and conclusions are false.

The point at which your research and conclusions are deemed falsified, is when your theories predictions are shown to be in error by a deviation of +/- 5 standard deviations points. To be considered an actual scientist conducting genuine scientific research, you must adhere to these principals. If your theory is deemed falsified, you are required to go back to the very beginning and start over.

That means, new observations, new questions, new hypothesis, new experiments and new conclusions.

What you do not get to do and still remain an actual genuine scientist, is keep readjusting your old observation, questions, hypothesis, experiments and conclusions until you get the results you want. That is scientific fraud.

Anthropogenic Global Warming aka Climate Change failed the falsification process literally dozens of times when every single one of its prediction were repeatedly shown to have a deviation greater than +/- 5 standard deviation points. More importantly, those engaged in attempting to foist this fraud on the general public knowingly and willfully violated the dictates of the Scientific Method which required that when the predictions of their theory were independently shown to be in error, they did not adhere to the principals of the Scientific Method and return to the beginning with all new observation, questions, hypothesis, experiments and conclusions.

So, #NeverTrump jerk and so called journalist Bret Stephens kicked the religious faithful, and is now getting mauled by them. That sucks for Bret, perhaps her should have stuck to writing safer political fiction and fantasy.